Chimaeras and mosaics for dissecting complex mutant phenotypes
Bibliographic record
Abstract
Back at the first half of the 1980s, there was no mammalian experimental embryology in Hungary. One of us, AN, took up the challenge of establishing a small group in the field. In the absence of local information, AN and his former colleague, Andras Paldi (AP), used their tourist passport to visit several laboratories in Western Europe and collect information and advice. This is how AN and AP ended up one day sitting in Anne McLaren's office in the MRC Mammalian Development Unit at University College, London. They never forgot her endless enthusiasm and the way she clearly explained the important points of preimplantation embryo manipulation, chimaera making and embryo transfer. As well as the extremely useful suggestions, which were crucial to starting the lab in Hungary, they also took back her deep love for embryo development. They remember her telling them, 'never waste an embryo--there is always another unanswered question it can solve'. Many who have been lucky and experienced Anne's spirit and advice later realized how useful it was to generate 'new' ideas by following the 'not wasting' principle. Our views on chimaeras presented below definitely contain elements which grew out from this principle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".